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train.sh
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#!/bin/bash
#Set Script Name variable
SCRIPT="train.sh"
print_help() {
printf "\nHelp documentation for ${BOLD}$SCRIPT ${NC}\n\n"
printf "The following cnd line options are recognized:\n"
printf " ${BOLD}-i ${NC}\t -- the input file coded in **UTF-8 without BOM**, containing the corpus for the training; the format of the input file is specified [here](https://github.com/collab-uniba/Emotion_and_Polarity_SO/wiki/File-format-for-training-corpus).\n"
printf " ${BOLD}-d ${NC}\t -- the delimiter semicolon or comma used in the csv file.\n"
printf " ${BOLD}-g ${NC}\t-- extract bigrams and unigrams (mandatory on the first run; extraction can be skipped afterwards for the same input file); dictionaries will be stored in `./training_filename/n-grams/UnigramsList.txt` and `./training_filename/n-grams/BigramsList.txt`).\n"
printf " ${BOLD}-p ${NC}\t-- wheter to calculate the feature of politeness, mood and modality.\n"
printf " ${BOLD}-e ${NC}\t -- the specific emotion for training the model, defined in joy, anger,sadness, love, surprise, fear.\n"
printf " ${BOLD}-X ${NC}\t -- the maximum amount of heap memory to use (e.g.: `-X 30000m`).\n"
printf " ${BOLD}-h ${NC}\t -- Displays this help message. No further functions are performed.\n\n"
printf "Example: ${BOLD} bash $SCRIPT -i path/file.csv -e anger -d semicolon -g ${NC}\n\n"
exit 1
}
# redefine an echo function depending on verbose switch
print() {
local level=$1
local code=$GREEN # default is "NOTICE"
if [ "${level}" = 'ERROR' ]; then
code=$RED
elif [ "${level}" = 'INFO' ]; then
code=$CYAN
fi
if [ "${VERBOSE}" == 'on' ]; then
printf "${code}[${level}]${NC}: " | tee -a $LOG
echo $2 | tee -a $LOG
else
printf "${code}[${level}]${NC}: "
echo $2
fi
}
EXTRACTDICTIONARY=""
CALCPOLITEIMPOLITEMOODMODALITY=""
# parse args
while getopts "i:d:e:gph" FLAG; do
case $FLAG in
i ) INPUT=$OPTARG;;
g ) EXTRACTDICTIONARY="-G";;
e ) EMOTION=$OPTARG
if [ "$EMOTION" != 'anger' ] && [ "$EMOTION" != 'fear' ] && [ "$EMOTION" != 'sadness' ] && [ "$EMOTION" != 'love' ] && [ "$EMOTION" != 'joy' ] && [ "$EMOTION" != 'surprise' ]; then
print "ERROR" "-e option has wrong argument."
exit 2;
fi;;
d ) DELIMITER=$OPTARG
if [ "$DELIMITER" != 'c' ] && [ "$DELIMITER" != 'sc' ] ; then
print "ERROR" "-d option has wrong argument."
exit 2;
fi;;
X ) MAX_HEAP_SIZE=$OPTARG;;
h ) print_help;;
p ) CALCPOLITEIMPOLITEMOODMODALITY="-polmod";;
\? ) #unrecognized option - show help
printf "INFO" "Use $SCRIPT -h to see the help documentation."
exit 2;;
: ) print "ERROR" "Missing option argument for -$OPTARG"
exit 2;;
* ) printf "ERROR" "Unimplemented option: -$OPTARG"
exit 2;;
esac
done
shift $((OPTIND-1)) #This tells getopts to move on to the next argument.
### end getopts code ###
filename=${INPUT##*/}
filename=${filename%.*}
if [ "$EXTRACTDICTIONARY" = '-G' ] ; then
print "training_$filename""_$EMOTION"
rm -rf "training_$filename""_$EMOTION" #if the same folder exists i will remove it
elif [ "$EXTRACTDICTIONARY" = '' ] ; then
if [ -d "training_$filename""_$EMOTION" ] ; then
cd "training_$filename""_$EMOTION"/ #if the same folder exists and he don't want re-extract the n-grams
if [ -d "n-grams" ] ; then
cd n-grams/
if [ -e 'BigramsList_1.txt' ] && [ -e 'UnigramsList_1.txt' ] && [ -e 'BigramsList_2.txt' ] && [ -e 'UnigramsList_2.txt' ] ; then #check the existence of the two files bigrams and unigrams
cd ..
find . -maxdepth 1 -not -name n-grams -not -name "." -exec rm -rf {} \; #removes all others directories
cd ..
else
print "ERROR" "UnigramsList.txt OR bigramsList.txt don't exist , I will extract the n-grams.."
EXTRACTDICTIONARY="-G"
cd ../..
rm -rf "training_$filename""_$EMOTION"
fi;
else
print "ERROR" "The folder n-grams doesn't exists. I will extract the n-grams.."
EXTRACTDICTIONARY="-G"
cd ..
rm -rf "training_$filename""_$EMOTION"
fi;
else
print "ERROR" "The folder "training_$filename""_$EMOTION" doesn't exist, extracting n-grams.."
EXTRACTDICTIONARY="-G"
fi;
fi;
# Create ids file
DELIMITER_CHAR=','
if [ "$DELIMITER" = 'sc' ] ; then
DELIMITER_CHAR=';'
fi;
awk -F "\"*"$DELIMITER_CHAR"\"*" '{print $1}' $INPUT | sed 's/^\"*//' > "training_$filename""_$EMOTION"/features-ids.csv
# Extract dictionary
if [ "$EXTRACTDICTIONARY" = '-G' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d "$DELIMITER_CHAR" $EXTRACTDICTIONARY -t training -Ex extractDictionary -e $EMOTION
fi;
#: <<'CREATEDOCFORMAT'
#Creating the format to give at python files.
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
if [ "$DELIMITER" = 'sc' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex createDocFormat -e $EMOTION
elif [ "$DELIMITER"='c' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex createDocFormat -e $EMOTION
fi;
# taking only the file.csv name, deleting path and the extension
# taking the files created for the two python files
cp "training_$filename""_$EMOTION"/ElaboratedFiles/docs.py python/CalculatePoliteAndImpolite/
cp "training_$filename""_$EMOTION"/ElaboratedFiles/docs.py python/CalculateMoodModality/
# starting python files for polite , impolite mood and modality extraction
cd python/CalculatePoliteAndImpolite
python model.py
rm docs.py
rm docs.pyc
cd ../..
cp python/CalculatePoliteAndImpolite/textsPoliteAndImpolite.csv "training_$filename""_$EMOTION"/ElaboratedFiles/
rm python/CalculatePoliteAndImpolite/textsPoliteAndImpolite.csv
cd python/CalculateMoodModality
python moodAndModality.py
rm docs.py
rm docs.pyc
cd ../..
cp python/CalculateMoodModality/textsMoodAndModality.csv "training_$filename""_$EMOTION"/ElaboratedFiles/
rm python/CalculateMoodModality/textsMoodAndModality.csv
fi;
#starting Emotion_and_Polarity_SO.jar to extract the features
if [ "$DELIMITER" = 'sc' ] ; then
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -P "training_$filename""_$EMOTION"/ElaboratedFiles/textsPoliteAndImpolite.csv -M "training_$filename""_$EMOTION"/ElaboratedFiles/textsMoodAndModality.csv -d ';' -t training -Ex SenPolImpolMoodModality -e $EMOTION
fi;
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex unigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex bigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex unigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex bigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ';' -t training -Ex wordnet -e $EMOTION
elif [ "$DELIMITER"='c' ] ; then
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -P "training_$filename""_$EMOTION"/ElaboratedFiles/textsPoliteAndImpolite.csv -M "training_$filename""_$EMOTION"/ElaboratedFiles/textsMoodAndModality.csv -d ',' -t training -Ex SenPolImpolMoodModality -e $EMOTION
fi;
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex unigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex bigrams_1 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex unigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex bigrams_2 -e $EMOTION
java -jar -Xmx${MAX_HEAP_SIZE:-"30000m"} -XX:+UseConcMarkSweepGC java/Emotion_and_Polarity_SO.jar -i $INPUT -d ',' -t training -Ex wordnet -e $EMOTION
fi;
#merging the single features extracted
if [ "$CALCPOLITEIMPOLITEMOODMODALITY" = '-polmod' ] ; then
paste -d , "training_$filename""_$EMOTION"/features-SenPolImpolMoodModality.csv "training_$filename""_$EMOTION"/features-unigrams_1.csv "training_$filename""_$EMOTION"/features-unigrams_2.csv "training_$filename""_$EMOTION"/features-bigrams_1.csv "training_$filename""_$EMOTION"/features-bigrams_2.csv "training_$filename""_$EMOTION"/features-wordnet.csv > "training_$filename""_$EMOTION"/features-$EMOTION.csv
else
paste -d , "training_$filename""_$EMOTION"/features-ids.csv "training_$filename""_$EMOTION"/features-unigrams_1.csv "training_$filename""_$EMOTION"/features-unigrams_2.csv "training_$filename""_$EMOTION"/features-bigrams_1.csv "training_$filename""_$EMOTION"/features-bigrams_2.csv "training_$filename""_$EMOTION"/features-wordnet.csv > "training_$filename""_$EMOTION"/features-$EMOTION.csv
fi;
#run the R script without downSamping (save the model) , and with downsampling(save the model)
#create a folder for the liblinear's generated outputs into the output folder
cd "training_$filename""_$EMOTION"
rm -rf liblinear
mkdir -p liblinear/DownSampling
mkdir -p liblinear/NoDownSampling
cd ..
mv "training_$filename""_$EMOTION"/features-$EMOTION.csv r/Liblinear/
cd r/Liblinear
rm -rf output/Results_$EMOTION
Rscript svmLiblinearWithoutDownSampling.R Results_$EMOTION modelsLiblinear.txt features-$EMOTION.csv $EMOTION
cd ../..
mv r/Liblinear/output/Results_$EMOTION/* "training_$filename""_$EMOTION"/liblinear/NoDownSampling/
rm -r r/Liblinear/output/Results_$EMOTION
#same thing with downSampling.
cd r/Liblinear
Rscript svmLiblinearDownSampling.R Results_$EMOTION modelsLiblinear.txt features-$EMOTION.csv $EMOTION
cd ../..
mv r/Liblinear/features-$EMOTION.csv "training_$filename""_$EMOTION"/
mv r/Liblinear/output/Results_$EMOTION/* "training_$filename""_$EMOTION"/liblinear/DownSampling/
rm -r r/Liblinear/output/Results_$EMOTION